The subject matter discussed in the background section should not be assumed to be prior art merely because of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Telecom operators have a complex order-to-cash value chain cutting across multiple systems and processes. Telecom services are one of the highly-digitized industries with networks generating humongous volume of data from multiple complex systems in a matter of a few minutes. Independent and comprehensive analysis of all the data is indispensable to assure all usage on network is reported in charging systems accurately. Such a comprehensive analysis of terabytes of data is neither possible by means of human surveillance nor by means of using conventional computing mechanisms.
A typical telecom operation consists of a long and complex chain of interrelated operations that work together to deliver telecommunication services to customers and then track the services delivered and bill the customers for the services delivered. As the set of technologies and business processes grow bigger and more complex, the chance of failure increases in each of its connections. Staying competitive in the telecommunications industry requires delivering quality voice and data services, responding rapidly to market demands, and maximizing revenues without affecting the underlying network. But to achieve these goals, a company must integrate large volumes of data in multiple formats from a wide range of systems—all while juggling technological changes such as 3G expansion and 4G/LTE network rollout and consolidation.
Revenue leakage or Data leakage is a big challenge faced by the telecom operators today. A revenue leakage caused due to data discrepancies is typically attributed to when a telecom operator is unable to bill correctly for a given service or to receive the correct payment due to several reasons. As the network grows the probability of such leakages only increase. Though there are a few systems that have been proposed for monitoring data and revenue leakage by using different algorithms, however, such systems are either possible only theoretically or require adding additional load on various network elements which is not desirable by any network operator. Moreover, it is very difficult for a network administrator to trace out a fraudulent user among the various users of the telecom network or a faulty network node or policy that may lead to data leakage.
Therefore, there is a long-standing need for a system and method to automatically capture subscriber usage data from the network independently without interfering with the operator's core network and reconcile such data with the data recorded by various nodes in the network, and charging systems to find out the revenue gaps and possible root-cause of the gaps which will help in measuring and minimizing the leakage.